Overview

Dataset statistics

Number of variables28
Number of observations656
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory118.0 KiB
Average record size in memory184.2 B

Variable types

Numeric17
Categorical11

Alerts

Price is highly overall correlated with RAM and 9 other fieldsHigh correlation
Weight is highly overall correlated with Screen Size and 1 other fieldsHigh correlation
Screen Size is highly overall correlated with Weight and 3 other fieldsHigh correlation
PPI is highly overall correlated with Resolution X and 7 other fieldsHigh correlation
RAM is highly overall correlated with Price and 8 other fieldsHigh correlation
Memory is highly overall correlated with Price and 3 other fieldsHigh correlation
FrontCamMP is highly overall correlated with RAMHigh correlation
Battery is highly overall correlated with Weight and 1 other fieldsHigh correlation
Age is highly overall correlated with Screen Size and 4 other fieldsHigh correlation
Resolution X is highly overall correlated with Price and 8 other fieldsHigh correlation
Resolution Y is highly overall correlated with PPI and 6 other fieldsHigh correlation
MaxSpeed is highly overall correlated with Price and 7 other fieldsHigh correlation
MaxVideoResolutionWidth is highly overall correlated with Price and 5 other fieldsHigh correlation
MaxVideoResolutionHeight is highly overall correlated with Price and 5 other fieldsHigh correlation
OpSys_version is highly overall correlated with Age and 4 other fieldsHigh correlation
Brand_encoded is highly overall correlated with Wireless Ch and 2 other fieldsHigh correlation
Refresh Rate is highly overall correlated with 5GHigh correlation
NoofCam is highly overall correlated with AgeHigh correlation
Wireless Ch is highly overall correlated with Price and 6 other fieldsHigh correlation
Fast Charging is highly overall correlated with Memory and 3 other fieldsHigh correlation
Fingerprint is highly overall correlated with Screen Size and 6 other fieldsHigh correlation
4G is highly overall correlated with Screen Size and 5 other fieldsHigh correlation
5G is highly overall correlated with Price and 4 other fieldsHigh correlation
MaxFrameRate is highly overall correlated with Price and 3 other fieldsHigh correlation
ScreenType_LCD is highly overall correlated with Price and 6 other fieldsHigh correlation
ScreenType_OLED is highly overall correlated with PPI and 2 other fieldsHigh correlation
Fast Charging is highly imbalanced (59.5%)Imbalance
Fingerprint is highly imbalanced (73.9%)Imbalance
Dual SIM is highly imbalanced (91.5%)Imbalance
4G is highly imbalanced (98.4%)Imbalance
MaxFrameRate is highly imbalanced (66.6%)Imbalance
ScreenType_OLED is highly imbalanced (63.9%)Imbalance
Age has 7 (1.1%) zerosZeros

Reproduction

Analysis started2023-07-11 11:15:41.342739
Analysis finished2023-07-11 11:16:16.135741
Duration34.79 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Price
Real number (ℝ)

Distinct296
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25247.867
Minimum10099
Maximum99900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2023-07-11T16:46:16.223605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10099
5-th percentile10999
Q114384.25
median18994
Q329990
95-th percentile62499
Maximum99900
Range89801
Interquartile range (IQR)15605.75

Descriptive statistics

Standard deviation16572.955
Coefficient of variation (CV)0.65641009
Kurtosis4.5497378
Mean25247.867
Median Absolute Deviation (MAD)6004.5
Skewness2.0725138
Sum16562601
Variance2.7466284 × 108
MonotonicityNot monotonic
2023-07-11T16:46:16.362818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13999 22
 
3.4%
14999 18
 
2.7%
17999 16
 
2.4%
10999 16
 
2.4%
15999 14
 
2.1%
11999 14
 
2.1%
13499 13
 
2.0%
24999 13
 
2.0%
12999 13
 
2.0%
19999 11
 
1.7%
Other values (286) 506
77.1%
ValueCountFrequency (%)
10099 1
 
0.2%
10199 1
 
0.2%
10294 1
 
0.2%
10400 1
 
0.2%
10489 1
 
0.2%
10490 1
 
0.2%
10499 3
0.5%
10500 1
 
0.2%
10699 2
0.3%
10758 1
 
0.2%
ValueCountFrequency (%)
99900 1
 
0.2%
97999 1
 
0.2%
94999 1
 
0.2%
91999 2
0.3%
89999 3
0.5%
89400 1
 
0.2%
84999 1
 
0.2%
83000 1
 
0.2%
81499 1
 
0.2%
80999 1
 
0.2%

Weight
Real number (ℝ)

Distinct113
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.52439
Minimum124
Maximum271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2023-07-11T16:46:16.499130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum124
5-th percentile157
Q1179
median189
Q3198
95-th percentile209
Maximum271
Range147
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.562791
Coefficient of variation (CV)0.088323394
Kurtosis1.6936617
Mean187.52439
Median Absolute Deviation (MAD)9.5
Skewness-0.36887037
Sum123016
Variance274.32603
MonotonicityNot monotonic
2023-07-11T16:46:16.641405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190 28
 
4.3%
195 26
 
4.0%
205 23
 
3.5%
182 23
 
3.5%
189 22
 
3.4%
192 21
 
3.2%
188 19
 
2.9%
186 18
 
2.7%
187 18
 
2.7%
179 15
 
2.3%
Other values (103) 443
67.5%
ValueCountFrequency (%)
124 1
0.2%
133 2
0.3%
138 1
0.2%
139 1
0.2%
140 2
0.3%
142 1
0.2%
142.6 1
0.2%
143 1
0.2%
144 2
0.3%
145 1
0.2%
ValueCountFrequency (%)
271 1
 
0.2%
229 1
 
0.2%
228 2
 
0.3%
227 1
 
0.2%
225 5
0.8%
221 4
0.6%
220 1
 
0.2%
219 1
 
0.2%
218 3
0.5%
216 2
 
0.3%

Screen Size
Real number (ℝ)

Distinct50
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4681707
Minimum4
Maximum7.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2023-07-11T16:46:16.793157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5.8075
Q16.43
median6.53
Q36.67
95-th percentile6.73
Maximum7.6
Range3.6
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.32941958
Coefficient of variation (CV)0.050929327
Kurtosis13.841505
Mean6.4681707
Median Absolute Deviation (MAD)0.12
Skewness-3.1185254
Sum4243.12
Variance0.10851726
MonotonicityNot monotonic
2023-07-11T16:46:16.934912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.5 74
 
11.3%
6.6 66
 
10.1%
6.7 63
 
9.6%
6.67 62
 
9.5%
6.4 44
 
6.7%
6.43 42
 
6.4%
6.44 26
 
4.0%
6.58 25
 
3.8%
6.1 22
 
3.4%
6.55 21
 
3.2%
Other values (40) 211
32.2%
ValueCountFrequency (%)
4 1
 
0.2%
4.5 1
 
0.2%
4.7 4
 
0.6%
5 2
 
0.3%
5.2 3
 
0.5%
5.4 4
 
0.6%
5.5 11
1.7%
5.6 1
 
0.2%
5.7 3
 
0.5%
5.8 3
 
0.5%
ValueCountFrequency (%)
7.6 1
 
0.2%
6.95 3
 
0.5%
6.9 2
 
0.3%
6.8 9
 
1.4%
6.78 15
 
2.3%
6.74 2
 
0.3%
6.73 3
 
0.5%
6.72 10
 
1.5%
6.71 2
 
0.3%
6.7 63
9.6%

PPI
Real number (ℝ)

Distinct70
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean387.83384
Minimum218
Maximum568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-07-11T16:46:17.086833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum218
5-th percentile269
Q1393
median400
Q3409
95-th percentile477
Maximum568
Range350
Interquartile range (IQR)16

Descriptive statistics

Standard deviation60.078078
Coefficient of variation (CV)0.15490674
Kurtosis1.0359931
Mean387.83384
Median Absolute Deviation (MAD)7
Skewness-0.55818845
Sum254419
Variance3609.3754
MonotonicityNot monotonic
2023-07-11T16:46:17.236367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
395 81
 
12.3%
409 72
 
11.0%
270 55
 
8.4%
400 50
 
7.6%
405 42
 
6.4%
401 33
 
5.0%
411 27
 
4.1%
393 24
 
3.7%
269 22
 
3.4%
402 20
 
3.0%
Other values (60) 230
35.1%
ValueCountFrequency (%)
218 1
 
0.2%
233 1
 
0.2%
258 1
 
0.2%
266 2
 
0.3%
267 5
 
0.8%
268 8
 
1.2%
269 22
 
3.4%
270 55
8.4%
274 3
 
0.5%
282 4
 
0.6%
ValueCountFrequency (%)
568 1
 
0.2%
566 1
 
0.2%
551 2
 
0.3%
531 3
 
0.5%
526 10
1.5%
524 1
 
0.2%
522 2
 
0.3%
521 3
 
0.5%
518 1
 
0.2%
516 1
 
0.2%

Refresh Rate
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
60
235 
120
229 
90
179 
144
 
13

Length

Max length3
Median length2
Mean length2.3689024
Min length2

Characters and Unicode

Total characters1554
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row90
2nd row90
3rd row60
4th row60
5th row60

Common Values

ValueCountFrequency (%)
60 235
35.8%
120 229
34.9%
90 179
27.3%
144 13
 
2.0%

Length

2023-07-11T16:46:17.387853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:46:17.526482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
60 235
35.8%
120 229
34.9%
90 179
27.3%
144 13
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 643
41.4%
1 242
 
15.6%
6 235
 
15.1%
2 229
 
14.7%
9 179
 
11.5%
4 26
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1554
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 643
41.4%
1 242
 
15.6%
6 235
 
15.1%
2 229
 
14.7%
9 179
 
11.5%
4 26
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1554
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 643
41.4%
1 242
 
15.6%
6 235
 
15.1%
2 229
 
14.7%
9 179
 
11.5%
4 26
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1554
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 643
41.4%
1 242
 
15.6%
6 235
 
15.1%
2 229
 
14.7%
9 179
 
11.5%
4 26
 
1.7%

RAM
Real number (ℝ)

Distinct9
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6112805
Minimum0
Maximum16
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-07-11T16:46:17.623143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14
median6
Q38
95-th percentile12
Maximum16
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4212408
Coefficient of variation (CV)0.36622872
Kurtosis0.76021901
Mean6.6112805
Median Absolute Deviation (MAD)2
Skewness0.72710044
Sum4337
Variance5.8624069
MonotonicityNot monotonic
2023-07-11T16:46:17.721162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8 233
35.5%
6 176
26.8%
4 169
25.8%
12 53
 
8.1%
3 16
 
2.4%
2 4
 
0.6%
16 3
 
0.5%
0 1
 
0.2%
1 1
 
0.2%
ValueCountFrequency (%)
0 1
 
0.2%
1 1
 
0.2%
2 4
 
0.6%
3 16
 
2.4%
4 169
25.8%
6 176
26.8%
8 233
35.5%
12 53
 
8.1%
16 3
 
0.5%
ValueCountFrequency (%)
16 3
 
0.5%
12 53
 
8.1%
8 233
35.5%
6 176
26.8%
4 169
25.8%
3 16
 
2.4%
2 4
 
0.6%
1 1
 
0.2%
0 1
 
0.2%

Memory
Real number (ℝ)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.03049
Minimum4
Maximum512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-07-11T16:46:17.821160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile64
Q164
median128
Q3128
95-th percentile256
Maximum512
Range508
Interquartile range (IQR)64

Descriptive statistics

Standard deviation73.302046
Coefficient of variation (CV)0.53886483
Kurtosis5.0214663
Mean136.03049
Median Absolute Deviation (MAD)0
Skewness1.6699652
Sum89236
Variance5373.1899
MonotonicityNot monotonic
2023-07-11T16:46:17.920157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
128 371
56.6%
64 142
 
21.6%
256 113
 
17.2%
32 18
 
2.7%
512 6
 
0.9%
16 5
 
0.8%
4 1
 
0.2%
ValueCountFrequency (%)
4 1
 
0.2%
16 5
 
0.8%
32 18
 
2.7%
64 142
 
21.6%
128 371
56.6%
256 113
 
17.2%
512 6
 
0.9%
ValueCountFrequency (%)
512 6
 
0.9%
256 113
 
17.2%
128 371
56.6%
64 142
 
21.6%
32 18
 
2.7%
16 5
 
0.8%
4 1
 
0.2%

NoofCam
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size37.3 KiB
3
347 
4
138 
2
132 
1
39 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters656
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row4
5th row4

Common Values

ValueCountFrequency (%)
3 347
52.9%
4 138
 
21.0%
2 132
 
20.1%
1 39
 
5.9%

Length

2023-07-11T16:46:18.027529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:46:18.147431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 347
52.9%
4 138
 
21.0%
2 132
 
20.1%
1 39
 
5.9%

Most occurring characters

ValueCountFrequency (%)
3 347
52.9%
4 138
 
21.0%
2 132
 
20.1%
1 39
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 656
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 347
52.9%
4 138
 
21.0%
2 132
 
20.1%
1 39
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 347
52.9%
4 138
 
21.0%
2 132
 
20.1%
1 39
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 347
52.9%
4 138
 
21.0%
2 132
 
20.1%
1 39
 
5.9%

MainCamMP
Real number (ℝ)

Distinct14
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.213415
Minimum5
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-07-11T16:46:18.248291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile12
Q148
median50
Q364
95-th percentile108
Maximum200
Range195
Interquartile range (IQR)16

Descriptive statistics

Standard deviation29.654708
Coefficient of variation (CV)0.59057342
Kurtosis5.8024889
Mean50.213415
Median Absolute Deviation (MAD)14
Skewness1.5452785
Sum32940
Variance879.40171
MonotonicityNot monotonic
2023-07-11T16:46:18.355469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
50 199
30.3%
64 130
19.8%
48 111
16.9%
12 72
 
11.0%
13 53
 
8.1%
108 52
 
7.9%
16 22
 
3.4%
200 7
 
1.1%
100 3
 
0.5%
5 2
 
0.3%
Other values (4) 5
 
0.8%
ValueCountFrequency (%)
5 2
 
0.3%
8 1
 
0.2%
12 72
 
11.0%
13 53
 
8.1%
16 22
 
3.4%
21 1
 
0.2%
25 2
 
0.3%
32 1
 
0.2%
48 111
16.9%
50 199
30.3%
ValueCountFrequency (%)
200 7
 
1.1%
108 52
 
7.9%
100 3
 
0.5%
64 130
19.8%
50 199
30.3%
48 111
16.9%
32 1
 
0.2%
25 2
 
0.3%
21 1
 
0.2%
16 22
 
3.4%

FrontCamMP
Real number (ℝ)

Distinct17
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.41311
Minimum2
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-07-11T16:46:18.471687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q18
median16
Q320
95-th percentile32
Maximum60
Range58
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.292297
Coefficient of variation (CV)0.59106597
Kurtosis2.1953349
Mean17.41311
Median Absolute Deviation (MAD)4
Skewness1.459734
Sum11423
Variance105.93137
MonotonicityNot monotonic
2023-07-11T16:46:18.569356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
16 229
34.9%
8 136
20.7%
32 97
14.8%
13 45
 
6.9%
20 37
 
5.6%
5 24
 
3.7%
12 21
 
3.2%
10 20
 
3.0%
50 11
 
1.7%
44 10
 
1.5%
Other values (7) 26
 
4.0%
ValueCountFrequency (%)
2 2
 
0.3%
5 24
 
3.7%
7 6
 
0.9%
8 136
20.7%
10 20
 
3.0%
11 1
 
0.2%
12 21
 
3.2%
13 45
 
6.9%
16 229
34.9%
20 37
 
5.6%
ValueCountFrequency (%)
60 4
 
0.6%
50 11
 
1.7%
44 10
 
1.5%
40 4
 
0.6%
32 97
14.8%
25 7
 
1.1%
24 2
 
0.3%
20 37
 
5.6%
16 229
34.9%
13 45
 
6.9%

Battery
Real number (ℝ)

Distinct78
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4642.032
Minimum1700
Maximum7000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-07-11T16:46:18.694207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1700
5-th percentile3007.5
Q14353.75
median5000
Q35000
95-th percentile6000
Maximum7000
Range5300
Interquartile range (IQR)646.25

Descriptive statistics

Standard deviation765.6512
Coefficient of variation (CV)0.1649388
Kurtosis1.7827375
Mean4642.032
Median Absolute Deviation (MAD)160
Skewness-0.9417035
Sum3045173
Variance586221.76
MonotonicityNot monotonic
2023-07-11T16:46:18.835345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 306
46.6%
4500 81
 
12.3%
6000 44
 
6.7%
4000 29
 
4.4%
4200 11
 
1.7%
3000 11
 
1.7%
5020 10
 
1.5%
4300 9
 
1.4%
3500 9
 
1.4%
3700 8
 
1.2%
Other values (68) 138
21.0%
ValueCountFrequency (%)
1700 1
0.2%
1821 2
0.3%
2000 1
0.2%
2018 2
0.3%
2227 2
0.3%
2406 2
0.3%
2550 1
0.2%
2600 2
0.3%
2650 1
0.2%
2800 1
0.2%
ValueCountFrequency (%)
7000 2
 
0.3%
6000 44
 
6.7%
5300 1
 
0.2%
5160 4
 
0.6%
5080 2
 
0.3%
5065 2
 
0.3%
5050 2
 
0.3%
5020 10
 
1.5%
5003 1
 
0.2%
5000 306
46.6%

Wireless Ch
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size37.3 KiB
0
571 
1
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 571
87.0%
1 85
 
13.0%

Length

2023-07-11T16:46:18.959420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:46:19.072139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 571
87.0%
1 85
 
13.0%

Most occurring characters

ValueCountFrequency (%)
0 571
87.0%
1 85
 
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 656
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 571
87.0%
1 85
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
Common 656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 571
87.0%
1 85
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 571
87.0%
1 85
 
13.0%

Fast Charging
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size37.3 KiB
1
603 
0
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 603
91.9%
0 53
 
8.1%

Length

2023-07-11T16:46:19.164816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:46:19.279542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 603
91.9%
0 53
 
8.1%

Most occurring characters

ValueCountFrequency (%)
1 603
91.9%
0 53
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 656
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 603
91.9%
0 53
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Common 656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 603
91.9%
0 53
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 603
91.9%
0 53
 
8.1%

Fingerprint
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size37.3 KiB
1
627 
0
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 627
95.6%
0 29
 
4.4%

Length

2023-07-11T16:46:19.376455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:46:19.490876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 627
95.6%
0 29
 
4.4%

Most occurring characters

ValueCountFrequency (%)
1 627
95.6%
0 29
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 656
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 627
95.6%
0 29
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 627
95.6%
0 29
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 627
95.6%
0 29
 
4.4%

Dual SIM
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size37.3 KiB
1
649 
0
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 649
98.9%
0 7
 
1.1%

Length

2023-07-11T16:46:19.587837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:46:19.703411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 649
98.9%
0 7
 
1.1%

Most occurring characters

ValueCountFrequency (%)
1 649
98.9%
0 7
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 656
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 649
98.9%
0 7
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 649
98.9%
0 7
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 649
98.9%
0 7
 
1.1%

Age
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.832317
Minimum0
Maximum99
Zeros7
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-07-11T16:46:19.815718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median18
Q333
95-th percentile60.25
Maximum99
Range99
Interquartile range (IQR)22

Descriptive statistics

Standard deviation18.11682
Coefficient of variation (CV)0.76017871
Kurtosis1.5968782
Mean23.832317
Median Absolute Deviation (MAD)10
Skewness1.2618658
Sum15634
Variance328.21917
MonotonicityNot monotonic
2023-07-11T16:46:19.961926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 36
 
5.5%
28 35
 
5.3%
14 29
 
4.4%
13 29
 
4.4%
16 25
 
3.8%
27 25
 
3.8%
17 25
 
3.8%
9 22
 
3.4%
2 21
 
3.2%
3 21
 
3.2%
Other values (68) 388
59.1%
ValueCountFrequency (%)
0 7
 
1.1%
1 6
 
0.9%
2 21
3.2%
3 21
3.2%
4 16
2.4%
5 18
2.7%
6 8
 
1.2%
7 1
 
0.2%
8 12
1.8%
9 22
3.4%
ValueCountFrequency (%)
99 1
0.2%
98 1
0.2%
94 1
0.2%
87 1
0.2%
86 1
0.2%
84 1
0.2%
83 1
0.2%
81 1
0.2%
77 1
0.2%
76 1
0.2%

Resolution X
Real number (ℝ)

Distinct11
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1041.1098
Minimum480
Maximum1768
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-07-11T16:46:20.078501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum480
5-th percentile720
Q11080
median1080
Q31080
95-th percentile1440
Maximum1768
Range1288
Interquartile range (IQR)0

Descriptive statistics

Standard deviation168.96372
Coefficient of variation (CV)0.16229194
Kurtosis1.6276125
Mean1041.1098
Median Absolute Deviation (MAD)0
Skewness-0.40101218
Sum682968
Variance28548.739
MonotonicityNot monotonic
2023-07-11T16:46:20.184865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1080 493
75.2%
720 102
 
15.5%
1440 33
 
5.0%
1170 10
 
1.5%
750 4
 
0.6%
828 4
 
0.6%
1260 3
 
0.5%
480 2
 
0.3%
1284 2
 
0.3%
1240 2
 
0.3%
ValueCountFrequency (%)
480 2
 
0.3%
720 102
 
15.5%
750 4
 
0.6%
828 4
 
0.6%
1080 493
75.2%
1170 10
 
1.5%
1240 2
 
0.3%
1260 3
 
0.5%
1284 2
 
0.3%
1440 33
 
5.0%
ValueCountFrequency (%)
1768 1
 
0.2%
1440 33
 
5.0%
1284 2
 
0.3%
1260 3
 
0.5%
1240 2
 
0.3%
1170 10
 
1.5%
1080 493
75.2%
828 4
 
0.6%
750 4
 
0.6%
720 102
 
15.5%

Resolution Y
Real number (ℝ)

Distinct40
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2283.9665
Minimum800
Maximum3216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-07-11T16:46:20.626207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum800
5-th percentile1560
Q12340
median2400
Q32400
95-th percentile2800
Maximum3216
Range2416
Interquartile range (IQR)60

Descriptive statistics

Standard deviation381.65532
Coefficient of variation (CV)0.16710198
Kurtosis1.3709097
Mean2283.9665
Median Absolute Deviation (MAD)8
Skewness-0.77268474
Sum1498282
Variance145660.78
MonotonicityNot monotonic
2023-07-11T16:46:20.757470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
2400 284
43.3%
2340 61
 
9.3%
1600 58
 
8.8%
2408 57
 
8.7%
2412 33
 
5.0%
1612 12
 
1.8%
1920 10
 
1.5%
2532 10
 
1.5%
2460 10
 
1.5%
2280 9
 
1.4%
Other values (30) 112
 
17.1%
ValueCountFrequency (%)
800 1
 
0.2%
854 1
 
0.2%
1280 6
 
0.9%
1334 4
 
0.6%
1440 5
 
0.8%
1520 7
 
1.1%
1544 4
 
0.6%
1560 6
 
0.9%
1600 58
8.8%
1612 12
 
1.8%
ValueCountFrequency (%)
3216 9
1.4%
3200 8
1.2%
3120 3
 
0.5%
3088 3
 
0.5%
3040 3
 
0.5%
2960 6
0.9%
2800 3
 
0.5%
2778 2
 
0.3%
2772 2
 
0.3%
2640 4
0.6%

4G
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size37.3 KiB
1
655 
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 655
99.8%
0 1
 
0.2%

Length

2023-07-11T16:46:20.875249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:46:20.985856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 655
99.8%
0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 655
99.8%
0 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 656
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 655
99.8%
0 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 655
99.8%
0 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 655
99.8%
0 1
 
0.2%

5G
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size37.3 KiB
0
345 
1
311 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 345
52.6%
1 311
47.4%

Length

2023-07-11T16:46:21.076459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:46:21.185852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 345
52.6%
1 311
47.4%

Most occurring characters

ValueCountFrequency (%)
0 345
52.6%
1 311
47.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 656
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 345
52.6%
1 311
47.4%

Most occurring scripts

ValueCountFrequency (%)
Common 656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 345
52.6%
1 311
47.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 345
52.6%
1 311
47.4%

MaxSpeed
Real number (ℝ)

Distinct35
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3848323
Minimum1.2
Maximum3.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2023-07-11T16:46:21.291821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.8
Q12.05
median2.3
Q32.7075
95-th percentile3.2
Maximum3.36
Range2.16
Interquartile range (IQR)0.6575

Descriptive statistics

Standard deviation0.41043225
Coefficient of variation (CV)0.17210109
Kurtosis-0.32378504
Mean2.3848323
Median Absolute Deviation (MAD)0.3
Skewness0.40281574
Sum1564.45
Variance0.16845463
MonotonicityNot monotonic
2023-07-11T16:46:21.414796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2 120
18.3%
2.2 97
14.8%
2.4 96
14.6%
2.3 60
9.1%
2.84 31
 
4.7%
2.05 31
 
4.7%
3 30
 
4.6%
3.2 26
 
4.0%
1.8 20
 
3.0%
2.6 19
 
2.9%
Other values (25) 126
19.2%
ValueCountFrequency (%)
1.2 1
 
0.2%
1.3 1
 
0.2%
1.4 6
 
0.9%
1.5 3
 
0.5%
1.6 3
 
0.5%
1.7 1
 
0.2%
1.8 20
 
3.0%
1.82 1
 
0.2%
1.95 2
 
0.3%
2 120
18.3%
ValueCountFrequency (%)
3.36 3
 
0.5%
3.23 13
2.0%
3.2 26
4.0%
3.1 8
 
1.2%
3.05 5
 
0.8%
3 30
4.6%
2.96 4
 
0.6%
2.91 4
 
0.6%
2.9 3
 
0.5%
2.85 13
2.0%

MaxVideoResolutionWidth
Real number (ℝ)

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3199.0244
Minimum1280
Maximum7680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2023-07-11T16:46:21.514795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1280
5-th percentile1920
Q11920
median3840
Q33840
95-th percentile7680
Maximum7680
Range6400
Interquartile range (IQR)1920

Descriptive statistics

Standard deviation1495.6112
Coefficient of variation (CV)0.46752103
Kurtosis2.3837338
Mean3199.0244
Median Absolute Deviation (MAD)1920
Skewness1.4576406
Sum2098560
Variance2236852.8
MonotonicityNot monotonic
2023-07-11T16:46:21.608408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3840 307
46.8%
1920 300
45.7%
7680 41
 
6.2%
2560 4
 
0.6%
5760 3
 
0.5%
1280 1
 
0.2%
ValueCountFrequency (%)
1280 1
 
0.2%
1920 300
45.7%
2560 4
 
0.6%
3840 307
46.8%
5760 3
 
0.5%
7680 41
 
6.2%
ValueCountFrequency (%)
7680 41
 
6.2%
5760 3
 
0.5%
3840 307
46.8%
2560 4
 
0.6%
1920 300
45.7%
1280 1
 
0.2%

MaxVideoResolutionHeight
Real number (ℝ)

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1799.4512
Minimum720
Maximum4320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2023-07-11T16:46:21.712272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum720
5-th percentile1080
Q11080
median2160
Q32160
95-th percentile4320
Maximum4320
Range3600
Interquartile range (IQR)1080

Descriptive statistics

Standard deviation841.28129
Coefficient of variation (CV)0.46752103
Kurtosis2.3837338
Mean1799.4512
Median Absolute Deviation (MAD)1080
Skewness1.4576406
Sum1180440
Variance707754.2
MonotonicityNot monotonic
2023-07-11T16:46:21.803511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2160 307
46.8%
1080 300
45.7%
4320 41
 
6.2%
1440 4
 
0.6%
3240 3
 
0.5%
720 1
 
0.2%
ValueCountFrequency (%)
720 1
 
0.2%
1080 300
45.7%
1440 4
 
0.6%
2160 307
46.8%
3240 3
 
0.5%
4320 41
 
6.2%
ValueCountFrequency (%)
4320 41
 
6.2%
3240 3
 
0.5%
2160 307
46.8%
1440 4
 
0.6%
1080 300
45.7%
720 1
 
0.2%

MaxFrameRate
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size37.9 KiB
30
594 
24
 
46
60
 
16

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1312
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row30
3rd row30
4th row30
5th row30

Common Values

ValueCountFrequency (%)
30 594
90.5%
24 46
 
7.0%
60 16
 
2.4%

Length

2023-07-11T16:46:21.917561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:46:22.030465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
30 594
90.5%
24 46
 
7.0%
60 16
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 610
46.5%
3 594
45.3%
2 46
 
3.5%
4 46
 
3.5%
6 16
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1312
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 610
46.5%
3 594
45.3%
2 46
 
3.5%
4 46
 
3.5%
6 16
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1312
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 610
46.5%
3 594
45.3%
2 46
 
3.5%
4 46
 
3.5%
6 16
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 610
46.5%
3 594
45.3%
2 46
 
3.5%
4 46
 
3.5%
6 16
 
1.2%

OpSys_version
Real number (ℝ)

Distinct13
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.059451
Minimum4
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2023-07-11T16:46:22.116884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q110
median11
Q312
95-th percentile13
Maximum16
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6451091
Coefficient of variation (CV)0.14875142
Kurtosis2.2182146
Mean11.059451
Median Absolute Deviation (MAD)1
Skewness-0.72687927
Sum7255
Variance2.7063838
MonotonicityNot monotonic
2023-07-11T16:46:22.219246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
11 214
32.6%
12 175
26.7%
10 82
 
12.5%
13 73
 
11.1%
9 50
 
7.6%
8 22
 
3.4%
7 11
 
1.7%
15 8
 
1.2%
5 5
 
0.8%
6 5
 
0.8%
Other values (3) 11
 
1.7%
ValueCountFrequency (%)
4 1
 
0.2%
5 5
 
0.8%
6 5
 
0.8%
7 11
 
1.7%
8 22
 
3.4%
9 50
 
7.6%
10 82
 
12.5%
11 214
32.6%
12 175
26.7%
13 73
 
11.1%
ValueCountFrequency (%)
16 5
 
0.8%
15 8
 
1.2%
14 5
 
0.8%
13 73
 
11.1%
12 175
26.7%
11 214
32.6%
10 82
 
12.5%
9 50
 
7.6%
8 22
 
3.4%
7 11
 
1.7%

ScreenType_LCD
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0.0
359 
1.0
297 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 359
54.7%
1.0 297
45.3%

Length

2023-07-11T16:46:22.329460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:46:22.444274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 359
54.7%
1.0 297
45.3%

Most occurring characters

ValueCountFrequency (%)
0 1015
51.6%
. 656
33.3%
1 297
 
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1312
66.7%
Other Punctuation 656
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1015
77.4%
1 297
 
22.6%
Other Punctuation
ValueCountFrequency (%)
. 656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1968
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1015
51.6%
. 656
33.3%
1 297
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1015
51.6%
. 656
33.3%
1 297
 
15.1%

ScreenType_OLED
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0.0
611 
1.0
 
45

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 611
93.1%
1.0 45
 
6.9%

Length

2023-07-11T16:46:22.540276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:46:22.648139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 611
93.1%
1.0 45
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 1267
64.4%
. 656
33.3%
1 45
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1312
66.7%
Other Punctuation 656
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1267
96.6%
1 45
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1968
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1267
64.4%
. 656
33.3%
1 45
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1267
64.4%
. 656
33.3%
1 45
 
2.3%

Brand_encoded
Real number (ℝ)

Distinct11
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25394.498
Minimum18504.425
Maximum65523.542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2023-07-11T16:46:22.728765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18504.425
5-th percentile18504.425
Q120240.621
median22107.037
Q330017.103
95-th percentile36682.923
Maximum65523.542
Range47019.117
Interquartile range (IQR)9776.4819

Descriptive statistics

Standard deviation9207.7109
Coefficient of variation (CV)0.36258685
Kurtosis10.533375
Mean25394.498
Median Absolute Deviation (MAD)2610.4078
Skewness3.0779001
Sum16658790
Variance84781941
MonotonicityNot monotonic
2023-07-11T16:46:22.827500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
30017.10256 117
17.8%
22850.07826 115
17.5%
21115.56701 97
14.8%
19496.62921 89
13.6%
22107.03704 54
8.2%
18504.425 40
 
6.1%
36682.92308 39
 
5.9%
25247.86738 32
 
4.9%
20240.62069 29
 
4.4%
65523.54167 24
 
3.7%
ValueCountFrequency (%)
18504.425 40
 
6.1%
19496.62921 89
13.6%
20177.73369 20
 
3.0%
20240.62069 29
 
4.4%
21115.56701 97
14.8%
22107.03704 54
8.2%
22850.07826 115
17.5%
25247.86738 32
 
4.9%
30017.10256 117
17.8%
36682.92308 39
 
5.9%
ValueCountFrequency (%)
65523.54167 24
 
3.7%
36682.92308 39
 
5.9%
30017.10256 117
17.8%
25247.86738 32
 
4.9%
22850.07826 115
17.5%
22107.03704 54
8.2%
21115.56701 97
14.8%
20240.62069 29
 
4.4%
20177.73369 20
 
3.0%
19496.62921 89
13.6%

Interactions

2023-07-11T16:46:13.764560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:43.739918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:45.658607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:47.725266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:49.649274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:51.633054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:53.475138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:55.224921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:57.258008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:59.036350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:00.808982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:02.748973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:04.548448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:06.272455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:08.028517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:09.883021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:11.980459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:13.861439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:43.841896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:45.762523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:47.835950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:49.753569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:51.733300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:53.573390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:55.330843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:57.355253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:59.139565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:00.902580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:02.848040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:04.642173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:06.381672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:08.131683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:09.983282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:12.077058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:13.980025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:43.961865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:45.881294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:47.953548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:49.871209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:51.846023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:53.683042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:55.447042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:57.465356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:59.245765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:01.008819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:02.957129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:04.747405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:06.500867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:08.243898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:10.097761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:12.185519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:14.087166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:44.078535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:46.002400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:48.072258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:49.984353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:51.959079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:53.794854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:55.559944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:57.575577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:59.357429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:01.115533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:03.069344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:04.860030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:06.621437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:08.359080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:10.212030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:12.293553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:14.189948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:44.205707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:46.232564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:48.187032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:50.089657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:52.065314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:53.896625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:55.673916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:57.679890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:59.463663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:01.224755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:03.174598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:04.965259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:06.732446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:08.471458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:10.321828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:12.397787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:14.299173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:44.339892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:46.358552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:48.307407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:50.204893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:52.179528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:54.005888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:55.953593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:57.790798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:59.572900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:01.337975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:03.288161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:05.071576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:06.845653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:08.593020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:10.435365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:12.507014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:14.398425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:44.443318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:46.468270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:48.419182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:50.310884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:52.287523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:54.103144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:56.060282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:57.890049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:59.674160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:01.434067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:03.392392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:05.170346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:06.939498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:08.697250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:10.790095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:12.609305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:14.505871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:44.557603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:46.593299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:48.536539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:50.433335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:52.402547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:54.212775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:56.173470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:58.005841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:59.784412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:01.545280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:03.507395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:05.279555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:07.042741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:08.812464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:10.908136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:12.722015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:14.607738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:44.662918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:46.707203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:48.647892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:50.670987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:52.512771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:54.319002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:56.283528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:58.113208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:59.888895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:01.647514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:03.611625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:05.381787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:07.142490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:08.921779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:11.015399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:12.832522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:14.708524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:44.766938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:46.819278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:48.756363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:50.775939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:52.622539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:54.417737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:56.390218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:58.214829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:59.988606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:01.747757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:03.716962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:05.483082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:07.239538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:09.025530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:11.120656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:12.934792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:14.805560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:44.873264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:46.930040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:48.861743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:50.880095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:52.724597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:54.513996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:56.494449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:58.315937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:00.090576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:01.844113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:03.819943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:05.580334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:07.334643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:09.128769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:11.224890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:13.038021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:14.911783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:44.989997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:47.043312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:48.972187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:50.993780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:52.834716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:54.621971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:56.604125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:58.423104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:00.199771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:01.946356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:03.924452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:05.682467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:07.434616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:09.236952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:11.334586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:13.145263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:15.008868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:45.100662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:47.153605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:49.076184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:51.096621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:52.934696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:54.716874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:56.708345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:58.525067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:00.295152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:02.041715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:04.020705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:05.773958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:07.542527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:09.337199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:11.432338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:13.241497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:15.102803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:45.212422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:47.260886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:49.177601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:51.195733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:53.030690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:54.813166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:56.810596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:58.617335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:00.387434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:02.131962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:04.116393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:05.863448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:07.629666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:09.433578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:11.532459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:13.336575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:15.213330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:45.333600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:47.383446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:49.300760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:51.308711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:53.145881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:54.919013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:56.928794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:58.727559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:00.498448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:02.242179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:04.227287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:05.970247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:07.733025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:09.541834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:11.647726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:13.447792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:15.320559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:45.452606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:47.504712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:49.424205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:51.423348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:53.260671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:55.026794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:57.043512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:58.835035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:00.608943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:02.552437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:04.338988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:06.074581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:07.836259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:09.660055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:11.762380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:13.558007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:15.423710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:45.561913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:47.619383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:49.539926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:51.534701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:53.372881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:55.127422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:57.154747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:45:58.941094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:00.712693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:02.654709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:04.449204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:06.177623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:07.937497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:09.779781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:11.872594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:46:13.665251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-11T16:46:22.961841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
PriceWeightScreen SizePPIRAMMemoryMainCamMPFrontCamMPBatteryAgeResolution XResolution YMaxSpeedMaxVideoResolutionWidthMaxVideoResolutionHeightOpSys_versionBrand_encodedRefresh RateNoofCamWireless ChFast ChargingFingerprintDual SIM4G5GMaxFrameRateScreenType_LCDScreenType_OLED
Price1.000-0.0860.0930.3930.6400.6340.0950.425-0.470-0.0630.5250.3340.7260.7050.7050.2580.3740.3210.0940.6980.1760.3830.0720.0000.5320.5000.5760.340
Weight-0.0861.0000.581-0.2560.1020.0980.219-0.0470.590-0.1820.0470.1880.0900.0280.0280.095-0.1380.1980.3620.3240.3290.3100.3780.4870.1610.2840.3210.259
Screen Size0.0930.5811.000-0.2820.3720.3070.4730.1750.495-0.5050.2380.4800.2640.1190.1190.373-0.2550.2790.4360.4890.3840.6270.4060.9940.3340.3490.1230.417
PPI0.393-0.256-0.2821.0000.2970.2670.0370.245-0.3110.0110.7490.5430.3240.3740.3740.1030.1820.2850.3420.6790.4970.6120.2960.6970.3880.5700.5020.585
RAM0.6400.1020.3720.2971.0000.7260.4430.527-0.071-0.2950.4540.4160.5480.5180.5180.2790.0290.3120.3720.1720.4690.2930.1590.7010.5050.2020.5440.000
Memory0.6340.0980.3070.2670.7261.0000.3190.381-0.065-0.3440.4100.3980.5690.4720.4720.3970.1100.2580.3550.3010.5660.3490.0700.1850.4920.2460.4730.222
MainCamMP0.0950.2190.4730.0370.4430.3191.0000.4330.330-0.4730.2000.3560.1580.1360.1360.350-0.2960.3210.3350.3760.4140.3640.1310.0000.3180.2000.3350.221
FrontCamMP0.425-0.0470.1750.2450.5270.3810.4331.000-0.149-0.0100.3420.2200.3100.4400.4400.002-0.0790.3200.3520.3790.4350.2810.0930.1330.2860.2510.4750.272
Battery-0.4700.5900.495-0.311-0.071-0.0650.330-0.1491.000-0.398-0.1350.125-0.190-0.332-0.3320.226-0.3060.2200.4290.4780.3330.6120.2960.3340.3440.3250.4520.343
Age-0.063-0.182-0.5050.011-0.295-0.344-0.473-0.010-0.3981.000-0.145-0.402-0.2950.0910.091-0.8740.0590.2400.5090.1230.5310.2570.2910.5650.4850.2740.2010.044
Resolution X0.5250.0470.2380.7490.4540.4100.2000.342-0.135-0.1451.0000.7720.4850.5020.5020.2270.0560.2610.2650.6590.4900.6130.2450.6990.3920.5510.4920.429
Resolution Y0.3340.1880.4800.5430.4160.3980.3560.2200.125-0.4020.7721.0000.4230.2900.2900.4270.0220.2790.4910.6120.6330.5640.4310.6970.4170.4770.5130.382
MaxSpeed0.7260.0900.2640.3240.5480.5690.1580.310-0.190-0.2950.4850.4231.0000.6650.6650.4510.1930.3180.3260.5010.4920.3790.1130.3320.6330.3930.5360.299
MaxVideoResolutionWidth0.7050.0280.1190.3740.5180.4720.1360.440-0.3320.0910.5020.2900.6651.0001.0000.0740.1940.3300.1280.4800.2780.1020.2200.0000.3630.4550.5590.317
MaxVideoResolutionHeight0.7050.0280.1190.3740.5180.4720.1360.440-0.3320.0910.5020.2900.6651.0001.0000.0740.1940.3300.1280.4800.2780.1020.2200.0000.3630.4550.5590.317
OpSys_version0.2580.0950.3730.1030.2790.3970.3500.0020.226-0.8740.2270.4270.4510.0740.0741.0000.1560.2800.4940.4360.5540.7390.0740.3900.5630.4420.2650.550
Brand_encoded0.374-0.138-0.2550.1820.0290.110-0.296-0.079-0.3060.0590.0560.0220.1930.1940.1940.1561.0000.1610.2870.6250.1660.7500.2830.0000.2250.4260.1880.613
Refresh Rate0.3210.1980.2790.2850.3120.2580.3210.3200.2200.2400.2610.2790.3180.3300.3300.2800.1611.0000.2080.1830.3320.2160.0380.0000.5600.1530.3560.228
NoofCam0.0940.3620.4360.3420.3720.3550.3350.3520.4290.5090.2650.4910.3260.1280.1280.4940.2870.2081.0000.2290.4530.3510.3440.1400.3640.1320.2520.243
Wireless Ch0.6980.3240.4890.6790.1720.3010.3760.3790.4780.1230.6590.6120.5010.4800.4800.4360.6250.1830.2291.0000.0990.3680.1990.0000.1610.6810.2620.460
Fast Charging0.1760.3290.3840.4970.4690.5660.4140.4350.3330.5310.4900.6330.4920.2780.2780.5540.1660.3320.4530.0991.0000.1350.0000.0460.2620.0780.3070.057
Fingerprint0.3830.3100.6270.6120.2930.3490.3640.2810.6120.2570.6130.5640.3790.1020.1020.7390.7500.2160.3510.3680.1351.0000.0000.0000.0000.4100.0000.424
Dual SIM0.0720.3780.4060.2960.1590.0700.1310.0930.2960.2910.2450.4310.1130.2200.2200.0740.2830.0380.3440.1990.0000.0001.0000.0000.0370.1680.0000.233
4G0.0000.4870.9940.6970.7010.1850.0000.1330.3340.5650.6990.6970.3320.0000.0000.3900.0000.0000.1400.0000.0460.0000.0001.0000.0000.0000.0000.000
5G0.5320.1610.3340.3880.5050.4920.3180.2860.3440.4850.3920.4170.6330.3630.3630.5630.2250.5600.3640.1610.2620.0000.0370.0001.0000.2670.3620.167
MaxFrameRate0.5000.2840.3490.5700.2020.2460.2000.2510.3250.2740.5510.4770.3930.4550.4550.4420.4260.1530.1320.6810.0780.4100.1680.0000.2671.0000.1960.324
ScreenType_LCD0.5760.3210.1230.5020.5440.4730.3350.4750.4520.2010.4920.5130.5360.5590.5590.2650.1880.3560.2520.2620.3070.0000.0000.0000.3620.1961.0000.238
ScreenType_OLED0.3400.2590.4170.5850.0000.2220.2210.2720.3430.0440.4290.3820.2990.3170.3170.5500.6130.2280.2430.4600.0570.4240.2330.0000.1670.3240.2381.000

Missing values

2023-07-11T16:46:15.603659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-11T16:46:15.986715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PriceWeightScreen SizePPIRefresh RateRAMMemoryNoofCamMainCamMPFrontCamMPBatteryWireless ChFast ChargingFingerprintDual SIMAgeResolution XResolution Y4G5GMaxSpeedMaxVideoResolutionWidthMaxVideoResolutionHeightMaxFrameRateOpSys_versionScreenType_LCDScreenType_OLEDBrand_encoded
013990206.06.604009041283501360000111210802408112.40192010803013.01.00.030017.102564
114990206.06.604009061283501360000111210802408112.40192010803013.01.00.030017.102564
210758207.06.60400604643508600001111110802408102.00192010803012.01.00.030017.102564
313999205.06.5027060464448850000111227201600102.00192010803011.01.00.030017.102564
417999182.06.4040360612846432400001114310802340102.2038402160309.00.00.021115.567010
510999157.05.7028260464112533000011647201440101.8019201080307.01.00.019496.629213
618390202.06.673951206128410816500001111510802400102.05192010803011.00.00.019496.629213
791999203.06.10457120651231212309511012111702532113.23384021602415.00.01.065523.541667
823499147.05.50401902161135260000019810801920101.7019201080305.01.00.025247.867378
918000168.05.50401604321138300000118710801920101.8019201080305.01.00.022850.078261
PriceWeightScreen SizePPIRefresh RateRAMMemoryNoofCamMainCamMPFrontCamMPBatteryWireless ChFast ChargingFingerprintDual SIMAgeResolution XResolution Y4G5GMaxSpeedMaxVideoResolutionWidthMaxVideoResolutionHeightMaxFrameRateOpSys_versionScreenType_LCDScreenType_OLEDBrand_encoded
64627999199.06.70393120825631083260000111010802400112.4384021603013.00.00.030017.102564
64718999172.06.384139061282641645000111210802400112.2192010803013.00.00.022850.078261
64839999205.06.7445112081283501650000111412402772113.2384021603013.00.00.036682.923077
64932999180.06.7838812081283505046000111310802400112.8384021603013.00.00.022850.078261
65024999172.56.70394120612831081650000111610802412112.6384021603013.00.00.021115.567010
65110999189.56.7239290464264850000111310802400102.0192010803013.01.00.021115.567010
65212999184.06.58401604128250850000111210802408112.2192010803013.01.00.022850.078261
65327999183.06.70394120825632003250000111010802412112.6384021603013.00.00.021115.567010
65419999195.06.72392120812831081650000111210802400112.2192010803013.01.00.036682.923077
65523999185.06.70394120812821001650000111010802412112.6384021603013.00.00.021115.567010